Criminal networks analysis in missing data scenarios through graph distances
Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same informati...
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2021-01-01
|
Series: | PLoS ONE |
Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357088/?tool=EBI |
id |
doaj-11c848f35e2c47729b66b384f6ea4022 |
---|---|
record_format |
Article |
spelling |
doaj-11c848f35e2c47729b66b384f6ea40222021-08-14T04:31:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01168Criminal networks analysis in missing data scenarios through graph distancesAnnamaria FicaraLucia CavallaroFrancesco CurreriGiacomo FiumaraPasquale De MeoOvidiu BagdasarWei SongAntonio LiottaData collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357088/?tool=EBI |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Annamaria Ficara Lucia Cavallaro Francesco Curreri Giacomo Fiumara Pasquale De Meo Ovidiu Bagdasar Wei Song Antonio Liotta |
spellingShingle |
Annamaria Ficara Lucia Cavallaro Francesco Curreri Giacomo Fiumara Pasquale De Meo Ovidiu Bagdasar Wei Song Antonio Liotta Criminal networks analysis in missing data scenarios through graph distances PLoS ONE |
author_facet |
Annamaria Ficara Lucia Cavallaro Francesco Curreri Giacomo Fiumara Pasquale De Meo Ovidiu Bagdasar Wei Song Antonio Liotta |
author_sort |
Annamaria Ficara |
title |
Criminal networks analysis in missing data scenarios through graph distances |
title_short |
Criminal networks analysis in missing data scenarios through graph distances |
title_full |
Criminal networks analysis in missing data scenarios through graph distances |
title_fullStr |
Criminal networks analysis in missing data scenarios through graph distances |
title_full_unstemmed |
Criminal networks analysis in missing data scenarios through graph distances |
title_sort |
criminal networks analysis in missing data scenarios through graph distances |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2021-01-01 |
description |
Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network. |
url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357088/?tool=EBI |
work_keys_str_mv |
AT annamariaficara criminalnetworksanalysisinmissingdatascenariosthroughgraphdistances AT luciacavallaro criminalnetworksanalysisinmissingdatascenariosthroughgraphdistances AT francescocurreri criminalnetworksanalysisinmissingdatascenariosthroughgraphdistances AT giacomofiumara criminalnetworksanalysisinmissingdatascenariosthroughgraphdistances AT pasqualedemeo criminalnetworksanalysisinmissingdatascenariosthroughgraphdistances AT ovidiubagdasar criminalnetworksanalysisinmissingdatascenariosthroughgraphdistances AT weisong criminalnetworksanalysisinmissingdatascenariosthroughgraphdistances AT antonioliotta criminalnetworksanalysisinmissingdatascenariosthroughgraphdistances |
_version_ |
1721207731713474560 |